{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sylvial/opt/anaconda3/lib/python3.9/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.7.3' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
      "/Users/sylvial/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.2' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import os "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import shapiro, ttest_ind, mannwhitneyu, levene\n",
    "import scipy.stats as stats\n",
    "\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.options.display.float_format = '{:.4f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
       "      <th>version</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>116</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>337</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>38</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>165</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>483</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>488</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>179</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid  version  sum_gamerounds  retention_1  retention_7\n",
       "0     116  gate_30               3        False        False\n",
       "1     337  gate_30              38         True        False\n",
       "2     377  gate_40             165         True        False\n",
       "3     483  gate_40               1        False        False\n",
       "4     488  gate_40             179         True         True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"cookie_cats.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 清除空缺值 clean missing data (if any). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 描述性统计 Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90189"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique players\n",
    "data['userid'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gate_30</th>\n",
       "      <td>44700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gate_40</th>\n",
       "      <td>45489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         userid\n",
       "version        \n",
       "gate_30   44700\n",
       "gate_40   45489"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of players in each group\n",
    "data.groupby('version')[['userid']].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum_gamerounds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>90189.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.8725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>195.0509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1%</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5%</th>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>3.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80%</th>\n",
       "      <td>67.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>134.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>221.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>493.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>49854.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sum_gamerounds\n",
       "count      90189.0000\n",
       "mean          51.8725\n",
       "std          195.0509\n",
       "min            0.0000\n",
       "1%             0.0000\n",
       "5%             1.0000\n",
       "10%            1.0000\n",
       "20%            3.0000\n",
       "50%           16.0000\n",
       "80%           67.0000\n",
       "90%          134.0000\n",
       "95%          221.0000\n",
       "99%          493.0000\n",
       "max        49854.0000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe([0.01, 0.05, 0.10, 0.20, 0.80, 0.90, 0.95, 0.99])[['sum_gamerounds']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gate_30</th>\n",
       "      <td>44700</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>52.4563</td>\n",
       "      <td>256.7164</td>\n",
       "      <td>49854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gate_40</th>\n",
       "      <td>45489</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>51.2988</td>\n",
       "      <td>103.2944</td>\n",
       "      <td>2640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count  median    mean      std    max\n",
       "version                                       \n",
       "gate_30  44700 17.0000 52.4563 256.7164  49854\n",
       "gate_40  45489 16.0000 51.2988 103.2944   2640"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('version')['sum_gamerounds'].agg(['count','median', 'mean', 'std', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(18,5))\n",
    "\n",
    "# histogram about gate30\n",
    "data[data.version == 'gate_30'].hist('sum_gamerounds', ax=axes[0])\n",
    "\n",
    "# histogram about gate40\n",
    "data[data.version == 'gate_40'].hist('sum_gamerounds', ax=axes[1])\n",
    "\n",
    "# compare\n",
    "sns.boxplot(x=data.version, y=data.sum_gamerounds, ax=axes[2])\n",
    "\n",
    "plt.suptitle('Before remove abnormal value', fontsize = 20)\n",
    "axes[0].set_title('Gate 30', fontsize = 13)\n",
    "axes[1].set_title('Gate 40', fontsize = 13)\n",
    "axes[2].set_title('Compare', fontsize = 13)\n",
    "\n",
    "plt.tight_layout(pad=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userid</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gate_30</th>\n",
       "      <td>44700</td>\n",
       "      <td>0.4482</td>\n",
       "      <td>0.1902</td>\n",
       "      <td>2344795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gate_40</th>\n",
       "      <td>45489</td>\n",
       "      <td>0.4423</td>\n",
       "      <td>0.1820</td>\n",
       "      <td>2333530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         userid  retention_1  retention_7  sum_gamerounds\n",
       "version                                                  \n",
       "gate_30   44700       0.4482       0.1902         2344795\n",
       "gate_40   45489       0.4423       0.1820         2333530"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_retention = data.groupby('version').agg({'userid':'count', 'retention_1':'mean', 'retention_7':'mean', 'sum_gamerounds':'sum'})\n",
    "df_retention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 移除异常值  remove the extreme(abnormal) value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum_gamerounds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>90188.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.3203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>102.6827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1%</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5%</th>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>3.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80%</th>\n",
       "      <td>67.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>134.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>221.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>493.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2961.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sum_gamerounds\n",
       "count      90188.0000\n",
       "mean          51.3203\n",
       "std          102.6827\n",
       "min            0.0000\n",
       "1%             0.0000\n",
       "5%             1.0000\n",
       "10%            1.0000\n",
       "20%            3.0000\n",
       "50%           16.0000\n",
       "80%           67.0000\n",
       "90%          134.0000\n",
       "95%          221.0000\n",
       "99%          493.0000\n",
       "max         2961.0000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = data[data.sum_gamerounds < data.sum_gamerounds.max()]\n",
    "data1.describe([0.01, 0.05, 0.10, 0.20, 0.80, 0.90, 0.95, 0.99])[['sum_gamerounds']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(18,5))\n",
    "\n",
    "# histgram about gate30\n",
    "data1[data1.version == 'gate_30'].hist('sum_gamerounds', ax=axes[0])\n",
    "\n",
    "# histgram about gate40\n",
    "data1[data1.version == 'gate_40'].hist('sum_gamerounds', ax=axes[1])\n",
    "\n",
    "# histgram about general\n",
    "data1.hist('sum_gamerounds', ax=axes[2])\n",
    "\n",
    "# compare\n",
    "sns.boxplot(x=data1.version, y=data1.sum_gamerounds, ax=axes[3])\n",
    "\n",
    "plt.suptitle('After remove abnormal value', fontsize = 20)\n",
    "axes[0].set_title('Gate 30', fontsize = 13)\n",
    "axes[1].set_title('Gate 40', fontsize = 13)\n",
    "axes[2].set_title('General', fontsize = 13)\n",
    "axes[3].set_title('Compare', fontsize = 13)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'After remove abnormal value')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reset index\n",
    "data1[data1.version == 'gate_30'].reset_index().sum_gamerounds.plot(legend=True, label = 'Gate 30', figsize = (20,5))\n",
    "data1[data1.version == 'gate_40'].reset_index().sum_gamerounds.plot(legend=True, label = 'Gate 40')\n",
    "plt.suptitle('After remove abnormal value', fontsize = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 数据洞察 Data Insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '# of player in each game round')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 1, figsize=(25,10))\n",
    "\n",
    "# 游戏每轮的用户数\n",
    "data1.groupby('sum_gamerounds').userid.count().plot(ax = axes[0])\n",
    "# 游戏前200轮的用户数\n",
    "data1.groupby('sum_gamerounds').userid.count()[:200].plot(ax = axes[1])\n",
    "# 游戏前100轮的用户数\n",
    "data1.groupby('sum_gamerounds').userid.count()[:100].plot(ax = axes[2])\n",
    "\n",
    "plt.suptitle('# of player in each game round', fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>user_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>1570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>1594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>1519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>1342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>1269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>1228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>1158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sum_gamerounds  user_cat\n",
       "0                0      3994\n",
       "1                1      5538\n",
       "2                2      4606\n",
       "3                3      3958\n",
       "4                4      3629\n",
       "5                5      2992\n",
       "6                6      2861\n",
       "7                7      2379\n",
       "8                8      2267\n",
       "9                9      2013\n",
       "10              10      1752\n",
       "11              11      1654\n",
       "12              12      1570\n",
       "13              13      1594\n",
       "14              14      1519\n",
       "15              15      1446\n",
       "16              16      1342\n",
       "17              17      1269\n",
       "18              18      1228\n",
       "19              19      1158"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.groupby('sum_gamerounds').userid.count().reset_index(name = 'user_cat').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sum_gamerounds\n",
       "30    642\n",
       "40    505\n",
       "Name: userid, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.groupby('sum_gamerounds').userid.count().loc[[30,40]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gate_30</th>\n",
       "      <td>44699</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>51.3421</td>\n",
       "      <td>102.0576</td>\n",
       "      <td>2961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gate_40</th>\n",
       "      <td>45489</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>51.2988</td>\n",
       "      <td>103.2944</td>\n",
       "      <td>2640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count  median    mean      std   max\n",
       "version                                      \n",
       "gate_30  44699 17.0000 51.3421 102.0576  2961\n",
       "gate_40  45489 16.0000 51.2988 103.2944  2640"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.groupby('version')['sum_gamerounds'].agg(['count','median', 'mean', 'std', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Multi-dimensional indexing (e.g. `obj[:, None]`) is no longer supported. Convert to a numpy array before indexing instead.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/mf/v90kzm9j347d6kx338s637d80000gn/T/ipykernel_10319/1909329649.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Histogram'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# 密度图\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum_gamerounds'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'version'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Density Plot'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# 盒图\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mkdeplot\u001b[0;34m(data, x, y, hue, weights, palette, hue_order, hue_norm, color, fill, multiple, common_norm, common_grid, cumulative, bw_method, bw_adjust, warn_singular, log_scale, levels, thresh, gridsize, cut, clip, legend, cbar, cbar_ax, cbar_kws, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1699\u001b[0m         \u001b[0mplot_kws\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1701\u001b[0;31m         p.plot_univariate_density(\n\u001b[0m\u001b[1;32m   1702\u001b[0m             \u001b[0mmultiple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmultiple\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1703\u001b[0m             \u001b[0mcommon_norm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcommon_norm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mplot_univariate_density\u001b[0;34m(self, multiple, common_norm, common_grid, warn_singular, fill, color, legend, estimate_kws, **plot_kws)\u001b[0m\n\u001b[1;32m    989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    990\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 991\u001b[0;31m                     \u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msupport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdensity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0martist_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    993\u001b[0m                 \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msticky_edges\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msticky_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1603\u001b[0m         \"\"\"\n\u001b[1;32m   1604\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1605\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1606\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1607\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    313\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[1;32m    488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxy\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 490\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    491\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_check_1d\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1360\u001b[0m                     message='Support for multi-dimensional indexing')\n\u001b[1;32m   1361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1362\u001b[0;31m                 \u001b[0mndim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1363\u001b[0m                 \u001b[0;31m# we have definitely hit a pandas index or series object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1364\u001b[0m                 \u001b[0;31m# cast to a numpy array.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   5417\u001b[0m         \u001b[0;31m# Because we ruled out integer above, we always get an arraylike here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5418\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5419\u001b[0;31m             \u001b[0mdisallow_ndim_indexing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5421\u001b[0m         \u001b[0;31m# NB: Using _constructor._simple_new would break if MultiIndex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexers/utils.py\u001b[0m in \u001b[0;36mdisallow_ndim_indexing\u001b[0;34m(result)\u001b[0m\n\u001b[1;32m    339\u001b[0m     \"\"\"\n\u001b[1;32m    340\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;34m\"Multi-dimensional indexing (e.g. `obj[:, None]`) is no longer \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0;34m\"supported. Convert to a numpy array before indexing instead.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Multi-dimensional indexing (e.g. `obj[:, None]`) is no longer supported. Convert to a numpy array before indexing instead."
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,2, figsize = (12,10))\n",
    "\n",
    "# 直方图\n",
    "sns.histplot(data = data1, x='sum_gamerounds', hue='version', kde=False, ax=axs[0,0])\n",
    "axs[0,0].set_title('Histogram')\n",
    "# 密度图\n",
    "sns.kdeplot(data = data1, x='sum_gamerounds', hue='version', ax=axs[0,1])\n",
    "axs[0,1].set_title('Density Plot')\n",
    "# 盒图\n",
    "sns.boxplot(data = data1, x='version', y='sum_gamerounds', ax=axs[1,0])\n",
    "axs[1,0].set_title('Box Plot')\n",
    "# ECDF图\n",
    "def ecdf(df):\n",
    "    x = sorted(df)\n",
    "    y = [i/len(x) for i in range(1, len(x)+1)]\n",
    "    return x, y\n",
    "\n",
    "gate_30_data = data1[data1['version'] == 'gate_30']['sum_gamerounds']\n",
    "gate_40_data = data1[data1['version'] == 'gate_40']['sum_gamerounds']\n",
    "\n",
    "x_g30, y_g30 = ecdf(gate_30_data)\n",
    "x_g40, y_g40 = ecdf(gate_40_data)\n",
    "\n",
    "axs[1,1].plot(x_g30, y_g30, label='gate 30')\n",
    "axs[1,1].plot(x_g40, y_g40, label='gate 40')\n",
    "axs[1,1].set_title('ECDF Plot')\n",
    "axs[1,1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ret1_count</th>\n",
       "      <th>ret1_ratio</th>\n",
       "      <th>ret7_count</th>\n",
       "      <th>ret7_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>50035</td>\n",
       "      <td>0.5548</td>\n",
       "      <td>73408</td>\n",
       "      <td>0.8139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>40153</td>\n",
       "      <td>0.4452</td>\n",
       "      <td>16780</td>\n",
       "      <td>0.1861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ret1_count  ret1_ratio  ret7_count  ret7_ratio\n",
       "False       50035      0.5548       73408      0.8139\n",
       "True        40153      0.4452       16780      0.1861"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#留存信息\n",
    "# retention_1： 玩家安装后第一天是否回来玩了\n",
    "# retention_7： 玩家安装后第七天是否回来玩了\n",
    "pd.DataFrame({'ret1_count': data1['retention_1'].value_counts(),\n",
    "    'ret1_ratio': data1['retention_1'].value_counts() / len(data1),\n",
    "    'ret7_count': data1['retention_7'].value_counts(),\n",
    "    'ret7_ratio': data1['retention_7'].value_counts() / len(data1)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <th>retention_1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">gate_30</th>\n",
       "      <th>False</th>\n",
       "      <td>24665</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>16.3591</td>\n",
       "      <td>36.5284</td>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>20034</td>\n",
       "      <td>48.0000</td>\n",
       "      <td>94.4117</td>\n",
       "      <td>135.0377</td>\n",
       "      <td>2961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">gate_40</th>\n",
       "      <th>False</th>\n",
       "      <td>25370</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>16.3404</td>\n",
       "      <td>35.9258</td>\n",
       "      <td>1241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>20119</td>\n",
       "      <td>49.0000</td>\n",
       "      <td>95.3812</td>\n",
       "      <td>137.8873</td>\n",
       "      <td>2640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  median    mean      std   max\n",
       "version retention_1                                      \n",
       "gate_30 False        24665  6.0000 16.3591  36.5284  1072\n",
       "        True         20034 48.0000 94.4117 135.0377  2961\n",
       "gate_40 False        25370  6.0000 16.3404  35.9258  1241\n",
       "        True         20119 49.0000 95.3812 137.8873  2640"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.groupby(['version','retention_1']).sum_gamerounds.agg(['count', 'median', 'mean', 'std', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <th>retention_7</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">gate_30</th>\n",
       "      <th>False</th>\n",
       "      <td>36198</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>25.7965</td>\n",
       "      <td>43.3162</td>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>8501</td>\n",
       "      <td>105.0000</td>\n",
       "      <td>160.1175</td>\n",
       "      <td>179.3586</td>\n",
       "      <td>2961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">gate_40</th>\n",
       "      <th>False</th>\n",
       "      <td>37210</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>25.8564</td>\n",
       "      <td>44.4061</td>\n",
       "      <td>2640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>8279</td>\n",
       "      <td>111.0000</td>\n",
       "      <td>165.6498</td>\n",
       "      <td>183.7925</td>\n",
       "      <td>2294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count   median     mean      std   max\n",
       "version retention_7                                        \n",
       "gate_30 False        36198  11.0000  25.7965  43.3162   981\n",
       "        True          8501 105.0000 160.1175 179.3586  2961\n",
       "gate_40 False        37210  11.0000  25.8564  44.4061  2640\n",
       "        True          8279 111.0000 165.6498 183.7925  2294"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.groupby(['version','retention_7']).sum_gamerounds.agg(['count', 'median', 'mean', 'std', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret1 = data1.groupby(['version','retention_1']).sum_gamerounds.agg(['count', 'median', 'mean', 'std', 'max']).reset_index()\n",
    "ret7 = data1.groupby(['version','retention_7']).sum_gamerounds.agg(['count', 'median', 'mean', 'std', 'max']).reset_index()\n",
    "\n",
    "sns.set(style='whitegrid')\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "ax1 = sns.barplot(data = ret1, x='version', y='count', hue='retention_1')\n",
    "plt.title('retention 1 comparison by Version')\n",
    "plt.xlabel('version')\n",
    "plt.ylabel('count')\n",
    "\n",
    "for p in ax1.patches:\n",
    "    ax1.annotate(f' {int(p.get_height())}', (p.get_x() + p.get_width()/2, p.get_height()),\n",
    "                            ha='center', va='center', fontsize=10, color='black', xytext=(0,5),\n",
    "                            textcoords='offset points')\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "ax2 = sns.barplot(data = ret7, x='version', y='count', hue='retention_7')\n",
    "plt.title('retention 7 comparison by Version')\n",
    "plt.xlabel('version')\n",
    "plt.ylabel('count')\n",
    "\n",
    "for p in ax2.patches:\n",
    "    ax2.annotate(f' {int(p.get_height())}', (p.get_x() + p.get_width()/2, p.get_height()),\n",
    "                            ha='center', va='center', fontsize=10, color='black', xytext=(0,5),\n",
    "                            textcoords='offset points')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <th>retention</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">gate_30</th>\n",
       "      <th>0</th>\n",
       "      <td>38023</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>28.0703</td>\n",
       "      <td>48.0175</td>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6676</td>\n",
       "      <td>127.0000</td>\n",
       "      <td>183.8863</td>\n",
       "      <td>189.6264</td>\n",
       "      <td>2961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">gate_40</th>\n",
       "      <th>0</th>\n",
       "      <td>38983</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>28.1034</td>\n",
       "      <td>48.9278</td>\n",
       "      <td>2640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6506</td>\n",
       "      <td>133.0000</td>\n",
       "      <td>190.2824</td>\n",
       "      <td>194.2201</td>\n",
       "      <td>2294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count   median     mean      std   max\n",
       "version retention                                        \n",
       "gate_30 0          38023  12.0000  28.0703  48.0175  1072\n",
       "        1           6676 127.0000 183.8863 189.6264  2961\n",
       "gate_40 0          38983  12.0000  28.1034  48.9278  2640\n",
       "        1           6506 133.0000 190.2824 194.2201  2294"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['retention'] = np.where((data1.retention_1 == True) & (data1.retention_7 == True), 1, 0) \n",
    "data1.groupby(['version','retention']).sum_gamerounds.agg(['count', 'median', 'mean', 'std', 'max'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>version</th>\n",
       "      <th>NewRetention</th>\n",
       "      <th>count</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>max</th>\n",
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       "      <th>0</th>\n",
       "      <td>gate_30</td>\n",
       "      <td>False-False</td>\n",
       "      <td>22840</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>11.8197</td>\n",
       "      <td>21.6426</td>\n",
       "      <td>981</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gate_30</td>\n",
       "      <td>False-True</td>\n",
       "      <td>1825</td>\n",
       "      <td>43.0000</td>\n",
       "      <td>73.1693</td>\n",
       "      <td>93.2223</td>\n",
       "      <td>1072</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gate_30</td>\n",
       "      <td>True-False</td>\n",
       "      <td>13358</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>49.6945</td>\n",
       "      <td>58.1254</td>\n",
       "      <td>918</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gate_30</td>\n",
       "      <td>True-True</td>\n",
       "      <td>6676</td>\n",
       "      <td>127.0000</td>\n",
       "      <td>183.8863</td>\n",
       "      <td>189.6264</td>\n",
       "      <td>2961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gate_40</td>\n",
       "      <td>False-False</td>\n",
       "      <td>23597</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>11.9133</td>\n",
       "      <td>20.9010</td>\n",
       "      <td>547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gate_40</td>\n",
       "      <td>False-True</td>\n",
       "      <td>1773</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>75.2611</td>\n",
       "      <td>94.4780</td>\n",
       "      <td>1241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gate_40</td>\n",
       "      <td>True-False</td>\n",
       "      <td>13613</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>50.0255</td>\n",
       "      <td>60.9246</td>\n",
       "      <td>2640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gate_40</td>\n",
       "      <td>True-True</td>\n",
       "      <td>6506</td>\n",
       "      <td>133.0000</td>\n",
       "      <td>190.2824</td>\n",
       "      <td>194.2201</td>\n",
       "      <td>2294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   version NewRetention  count   median     mean      std   max\n",
       "0  gate_30  False-False  22840   6.0000  11.8197  21.6426   981\n",
       "1  gate_30   False-True   1825  43.0000  73.1693  93.2223  1072\n",
       "2  gate_30   True-False  13358  33.0000  49.6945  58.1254   918\n",
       "3  gate_30    True-True   6676 127.0000 183.8863 189.6264  2961\n",
       "4  gate_40  False-False  23597   6.0000  11.9133  20.9010   547\n",
       "5  gate_40   False-True   1773  47.0000  75.2611  94.4780  1241\n",
       "6  gate_40   True-False  13613  32.0000  50.0255  60.9246  2640\n",
       "7  gate_40    True-True   6506 133.0000 190.2824 194.2201  2294"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['NewRetention'] = list(map(lambda x,y : str(x) + '-' + str(y), data1.retention_1, data1.retention_7))\n",
    "data1.groupby(['version','NewRetention']).sum_gamerounds.agg(['count', 'median', 'mean', 'std', 'max']).reset_index()"
   ]
  }
 ],
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